×

A random-effects model for clustered circular data. (English. French summary) Zbl 1522.62042

Summary: This article considers a circular regression model for clustered data, where both the cluster effects and the regression errors have von Mises distributions. It involves \(\beta\), a vector of parameters for the fixed effects, and two concentration parameters for the error distribution. A measure of intra-cluster circular correlation and a predictor for an unobserved cluster random effect are studied. Preliminary estimators for the vector \(\beta\) and the two concentration parameters are proposed, and their performance is compared with that of the maximum likelihood estimators in a simulation study. A numerical example investigating the factors impacting the orientation taken by a sand hopper when released is presented.

MSC:

62H11 Directional data; spatial statistics
62P10 Applications of statistics to biology and medical sciences; meta analysis
Full Text: DOI

References:

[1] Abe, T. & Pewsey, A. (2011). Sine‐skewed circular distributions. Statistical Papers, 52, 683-707. · Zbl 1434.62023
[2] Agostinelli, C. & Lund, U.2013. R package circular: Circular Statistics (version 0.4‐7), https://r-forge.r-project.org/projects/circular/
[3] Artes, R., Paula, G. A., & Ranvaud, R. (2000). Analysis of circular longitudinal data based on generalized estimating equations. Australian and New Zealand Journal of Statistics, 42, 347-358. · Zbl 0962.62051
[4] Battese, G. E., Harter, R. M., & Fuller, W. A. (1988). An error‐components model for prediction of county crop areas using survey and satellite data. Journal of the American Statistical Association, 83, 28-36.
[5] Cremers, J., Mulder, K. E., & Klugkist, I. (2018). Circular interpretation of regression coefficients. British Journal of Mathematical and Statistical Psychology, 71, 75-95. · Zbl 1460.62104
[6] D’Elia, A. (2001). A statistical model for orientation mechanism. Statistical Methods and Applications, 10, 157-174. · Zbl 1154.62351
[7] Downs, T. D. & Mardia, K. V. (2002). Circular regression. Biometrika, 89, 683-697. · Zbl 1037.62056
[8] Fisher, N. I. & Lee, A. J. (1992). Regression models for an angular response. Biometrics, 48, 665-677.
[9] Hall, D. B. & Shen, J. (2015). Marginal projected multivariate linear models for clustered angular data. Australian and New Zealand Journal of Statistics, 57, 241-257. · Zbl 1336.62181
[10] Hernandez‐Stumpfhauser, D., Breidt, F. J., & van derWoerd, M. J. (2017). The general projected normal distribution of arbitrary dimension: Modeling and Bayesian inference. Bayesian Analysis, 12, 113-133. · Zbl 1384.62176
[11] Holmquist, B. & Gustafsson, P. M. J. (2017). A two‐level directional model for dependence in circular data. Canadian Journal of Statistics, 45, 461-478. · Zbl 1474.62179
[12] Jammalamadaka, S. R. & Sarma, Y. R. (1988). A correlation coefficient for angular variables. In K.Matusita (ed.) (Ed.) Statistical Theory and Data Analysis II, Amsterdam: North Holland, 349-364. · Zbl 0738.62069
[13] Jona‐Lasinio, G., Gelfand, A. F., & Jona‐Lasinio, M. (2012). Spatial analysis of wave direction data using wrapped Gaussian processes. Annals of Applied Statistics, 6, 1478-1498. · Zbl 1257.62094
[14] Jones, M. C., Pewsey, A., & Kato, S. (2015). On a class of circulas: Copulas for circular distributions. Annals of the Institute of Statistical Mathematics, 67, 843-862. · Zbl 1440.62186
[15] Kato, S., Shimizu, K., & Shieh, G. S. (2008). A circular-circular regression model. Statistica Sinica, 18, 633-646. · Zbl 1135.62044
[16] Kato, S. & Jones, M. C. (2015). A tractable and interpretable four‐parameter family of unimodal distributions on the circle. Biometrika, 102, 181-190. · Zbl 1345.62020
[17] Lagona, F. (2016). Regression analysis of correlated circular data based on the multivariate von Mises distribution. Environmental and Ecological Statistics, 23, 89-113.
[18] Mardia, K. V., Hughes, G., Taylor, C. C., & Singh, H. (2008). A multivariate von Mises distribution with applications to bioinformatics. Canadian Journal of Statistics, 36, 99-109. · Zbl 1143.62031
[19] Mardia, K. V. & Jupp, P. E. (2000). Directional Statistics. Wiley, New York. · Zbl 0935.62065
[20] Maruotti, A. (2016). Analyzing longitudinal circular data by projected normal models: A semi‐parametric approach based on finite mixture models. Environmental and Ecological Statistics, 23, 257-277.
[21] Maruotti, A., Punzo, A., Mastrantonio, G., & Lagona, F. (2016). A time‐dependent extension of the projected normal regression model for longitudinal circular data based on a hidden Markov heterogeneity structure. Stochastic Environmental Research and Risk Assessment, 30, 1725-1740.
[22] Mastrantonio, G., Lasinio, G. J., & Gelfand, A. E. (2016). Spatio‐temporal circular models with non‐separable covariance structure. Test, 25, 331-350. · Zbl 1402.62102
[23] Nicosia, A., Duchesne, T., Rivest, L‐P., & Fortin, D. (2017). A general hidden state random walk model for animal movement. Computational Statistics and Data Analysis, 105, 76-95. · Zbl 1466.62168
[24] Nuñez‐Antonio, G. & Gutiérrez‐Peña, E. (2014). A Bayesian model for longitudinal circular data based on the projected normal distribution. Computational Statistics and Data Analysis, 71, 506-519. · Zbl 1471.62153
[25] Pewsey, A., Neuhäuser, M., & Ruxton, G. D. (2013). Circular Statistics in R. Oxford University Press, Oxford. · Zbl 1282.62137
[26] Presnell, B., Morrison, S. P., & Littell, R. (1998). Projected multivariate linear models for directional data. Journal of the American Statistical Association, 93, 1068-1077. · Zbl 1063.62546
[27] Rao, J. N. K. & Molina, I. (2015). Small Area Estimation, 2nd ed.Wiley, New York. · Zbl 1323.62002
[28] Rivest, L‐P. (1982). Some statistical methods for bivariate circular data. Journal of the Royal Statistical Society Series B, 44, 81-90. · Zbl 0494.62059
[29] Rivest, L‐P., Duchesne, T., Nicosia, A., & Fortin, D. (2016). Ageneral angular regression model for the analysis of data on animal movement in Ecology. Journal of the Royal Statistical Society, Series C, 66, 445-463.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.